Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Employer
- Delft University of Technology (TU Delft)
- CNRS
- Delft University of Technology (TU Delft); 17 Oct ’25 published
- Delft University of Technology (TU Delft); yesterday published
- ISCTE - Instituto Universitário de Lisboa
- Tallinn University of Technology
- Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
- Cranfield University
- Delft University of Technology (TU Delft); Delft
- Forschungszentrum Jülich
- Fraunhofer-Gesellschaft
- Heraeus Covantics
- Linköping University
- Lulea University of Technology
- Technical University of Denmark
- Universidad de Alicante
- University of East Anglia
- University of Groningen
- Uppsala universitet
- Vrije Universiteit Brussel
- 10 more »
- « less
-
Field
-
selectivity is the first important barrier to overcome in order to perform quantitative analyses for each pollutant and avoid ionic interference between the different sensors used in the project. Sensor
-
across different imaging devices, including future sensors with unknown spectral sensitivities. Training The student will be based at the Colour & Imaging Lab at the School of Computing Sciences which has
-
to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track mutations in evolving populations
-
. For example, we would like to be able to track how the prevalences of different strains in a mixed sample change over time. Your role: You will develop and implement algorithms to find, quantify and track
-
communication Autonomous driving algorithms and technologies (e.g. vehicle control, path planning, scheduling) and sensors (e.g. lidars, radars, cameras, and GNSS) High-level integration of autonomous driving
-
on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors for water quality monitoring do not
-
description Cities depend on sufficient and sufficiently clean water. However, we often lack the data to fully understand the dynamics of contaminants throughout the urban water cycle. Existing sensors
-
the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
-
3 Oct 2025 Job Information Organisation/Company Universidad de Alicante Department Department of Mathematics Research Field Mathematics » Algorithms Researcher Profile First Stage Researcher (R1
-
spanning design, modelling and simulation of photonic systems, sensor systems, signal processing and device manufacturing, development of machine learning algorithms, and design of optical communication